Many steps lay between the research of machine learning algorithms, and the delivery of effective solutions to real world manufacturing problems. Some of the requirements are to enable repeatable, meaningful ways of evaluating the performance of solutions, to efficiently manage the storage and versioning of models, and to effectively deliver predictive or analytic capabilities either on the edge or in the cloud.

This talk will cover some of the approaches, either successful or unsuccessful, taken by Eigen Innovations to present solutions to these problems as they occur in the context of developing autonomous injection moulding systems.


Speaker: Elliot Layne

Suggested Experience: Minimal to moderate, will not be delving into underlying mathematical theory.

Technologies Used: Tensorflow, AWS, Python, C++

Keywords: machine learning, autonomous manufacturing, dev ops, IIOT

Integration of ML Solutions in Industrial Manufacturing